Risk Of Non-point Source Pollution Research Articles

Assessing spatiotemporal risks of nonpoint source pollution via soil erosion: a coastal case in the Yellow River Delta, China.

Nonpoint source pollution (NPSP) has always been the dominant threat to regional waters. Based on empirical models of the revised universal soil loss equation and the phosphorus index, an NPSP risk assessment model denoted as SL-NPSRI was developed. The surface soil pollutant loss was estimated by simulating the rain-runoff topographic process, and the influence of path attenuation was quantified. A case study in the Yellow River Delta and corresponding field surveys of soil pollutants and water quality showed that the established model can be applied to evaluate the spatial heterogeneity of NPSP. NPSP usually occurs during high-intensity rainfall periods and in larger estuaries. Summer rainfall increased pollutant transport into the sea from late July to mid-August and caused estuarine dilution. Higher NPSP risks often correspond to coastal areas with lower vegetation coverage, higher soil erodibility, and higher soil pollutant concentrations. Agricultural NPSP originating from cropland significantly increase the pollutant fluxes. Therefore, area-specific land use management and vegetation coverage improvement, and temporal-specific strategies can be explored for NPSP control during source-transport hydrological processes. This research provides a novel insight for coastal NPSP simulations by comprehensively analyzing the soil erosion process and its associated pollutant loss effects, which can be useful for targeted spatiotemporal solutions.

Strategies for the management of water and nitrogen interaction in seed maize production; A case study from China Hexi Corridor Oasis Agricultural Area

The oasis agricultural area of the Hexi Corridor in China supports industrial-scale seed maize production which depends on significant inputs of water and fertilizer, however, the effects of water-nitrogen interaction on seed maize yield and its components, and on water and nitrogen use efficiency in seed maize are unknown, making optimization of inputs impossible. Here, we conducted a study to address these unknowns. Specifically, we established a two-factor split-plot experiment in the Zhangye area of the Hexi Corridor with three irrigation levels of low, medium, and sufficient water supply (50%, 75%, and 100% of the local typical irrigation quota), and three nitrogen application rates of low (160 kg ha−1), medium (280 kg ha−1), and high nitrogen (400 kg ha−1); three irrigation levels without nitrogen fertilizer were used as the control. We found that there was a significant interaction between water-nitrogen combinations, all of which had a significant effect on seed maize yield and its components, and water and nitrogen use efficiency. The yield of seed maize increased with the increase in irrigation quota, but when the irrigation quota reached 100% of the traditional, yield exhibited an increasing and then decreasing trend with the increase in nitrogen application. During the 4-year test period, the maximum yield of seed maize was observed in sufficient water and medium-nitrogen treatment, reaching an average of 9509.38 kg ha−1. Lower nitrogen lowered water productivity, while lower irrigation quotas limited nitrogen utilization. The relationship between irrigation quota, nitrogen application, and seed maize yield was binary quadratic, and the coefficient of determination, R2, reached 0.963–0.985 indicating that the model predicted the yield of seed maize well. The main factor analysis of yield increase showed that the effect of irrigation quota was greater than that of nitrogen application. The optimal combination of water-nitrogen for the highest target yield was obtained with an irrigation quota of 100% of the local typical and a nitrogen application rate of 292.0 kg ha−1-383.2 kg ha−1. This result can be used as a reasonable water and fertilizer management guideline and a technical reference for the seed maize industry in the oasis agricultural area of the Hexi Corridor with objectives of stable and high yields, and reductions in the risk of nitrogen non-point source pollution.

The regulation effect of preventing soil nitrogen loss using microbial quorum sensing inhibitors

Preventing soil nitrogen (N) losses driven by microbial nitrification and denitrification contributes to improving global environmental concerns caused by NO3−-N leaching and N2O emission. Quorum sensing (QS) signals regulate nitrification and denitrification of N-cycling bacteria in pure culture and water treatment systems, and mediate the composition of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) in activated sludge. However, whether disrupting QS could prevent soil N losses remains unclear. This study explored the feasibility of applying quorum sensing inhibitors (QSIs) as an innovative strategy to reduce N losses from agricultural soils. The two QSIs, penicillic acid and 4-iodo-N-[(3S)-tetrahydro-2-oxo-3-furanyl]-benzeneacetamide (4-iodo PHL), were more effective in reducing N losses than traditional inhibitors, including N-(n-butyl) thiophosphoric triamide and 3,4-dimethylpyrazole phosphate. After 36 days of aerobic incubation, penicillic acid and 4-iodo PHL inhibited nitrification by 39% and 68%, respectively. The inhibitory effects are attributed to the fact that 4-iodo PHL decreased the abundance of archaeal and bacterial amoA genes, as well as the relative abundance of Candidatus Nitrocosmicus (AOA), Candidatus Nitrososphaera (AOA), and Nitrospira (nitrite-oxidizing bacteria/comammox), while penicillic acid reduced archaeal amoA abundance and the relative abundance of Nitrosospira (AOB) and the microbes listed above. Penicillic acid also strongly inhibited denitrification (33%) and N2O emissions (61%) at the peak of N2O production (day 4 of anaerobic incubation) via decreasing nitrate reductase gene (narG) abundance and increasing N2O reductase gene (nosZ) abundance, respectively. Furthermore, the environmental risks of QSIs to microbial community structure and network stability, CO2 emissions, and soil animals were acceptable. Overall, QSIs have application potential in agriculture to reduce soil N losses and the associated effect on climate change. This study established a new method to mitigate N losses from the perspective of QS, and can serve as important basis of decreasing the environmental risks of agricultural non-point source pollution.

Effects of source-sink landscape proximity on the spatial–temporal water quality from the perspective of cost distance in an agricultural watershed

Spatial patterns of source-sink landscapes play a critical role in assessing surface water quality. However, this relation may be different due to various proximity of source-sink landscapes. The effect of the source-sink landscape proximity on surface water quality remains poorly understood. The impact of source-sink landscape proximity on the spatial–temporal water quality were quantified based on field monitoring data (2013–2019) in an agricultural watershed. A new approach considering the cost distance of landscape types, slope, and vegetation on the transformation of nutrients from one point to water bodies was used to construct different buffer zones which reflected very high, high, moderate, low, very low proximities, while the subwatershed represented the very low proximity. Then the effects of the source-sink landscape proximity were assessed based on sub-watershed and other four buffers. The resutls showed that the TN and NO3−-N were the main non-point source pollution indices with significant spatial variation (p < 0.05). The water quality index (WQI) was higher during the dry season than during the wet season, indicating that the sampling sites also experienced seasonal variations. The multi-scale analysis demonstrated the significance of the source-sink landscape proximity in improving the quality of surface water. A stronger correlation between the ratios of sink landscape and the main water quality parameters (the TN and NO3−-N) was observed in the low proximity zone, suggestiong that the low proximity zone with moderate non-point source pollution risk was a critical region in water quality management. The results provide important insights into understanding the effects of the source-sink landscape proximity on water quality and how scientific and effective measures can be made to improve water quality.

Accounting for the annual variability when assessing non-point source pollution potential in Mediterranean regulated watersheds

The characterization of non-point source pollution at the watershed scale difficult owing to its distributed nature combined with the lack of suitable measurements for validation. This study proposes the classification of land within a Mediterranean watershed according to its potential source of non-point pollution, considering interannual precipitation variability and dam regulation effects. For this purpose, the potential non-point pollution index (PNPI) developed by the Italian Environmental Protection Agency was modified to include annual local precipitation behavior, named local annual PNPI (APNPI). PNPI and APNPI were computed for the Guadalquivir River (Spain), which has a drainage surface of 57,500 km2 and is highly regulated by >60 reservoirs. The results reflect the vulnerability along the Guadalquivir River in terms of the spatially variable non-point pollutant nature of its contributing watersheds. The annual average nitrate concentration values on the southern side exceeded the average value on the northern side by almost five times and showed a statistically significant power fit with the PNPI, with an R2 of 0.65. Long-term available nitrate data (1981/82–2006/07) on a monthly scale at the outlets of some watersheds allowed us to rank priority pollutant source areas within the watershed. The power fits between the annual average nitrate loads and the APNPI (R2 = 0.51–0.99) were statistically significant, which validated the utility of adding the variability of precipitation at an annual scale as a dynamic factor in the index. The APNPI can constitute a simple dynamic classification index for assessing the relative risk of non-point source pollution across a large area, especially in data-scarce situations.

Spatiotemporal variations and risk characteristics of potential non-point source pollution driven by LUCC in the Loess Plateau Region, China

With increasing human activities, regional substrate conditions have undergone significant changes. These changes have resulted in temporal and spatial variations of non-point source pollution sources, which has a significant impact on the quality of the regional soil, surface water, and groundwater environments. This study focused on the human-disturbed Loess Plateau region and used an enhanced potential non-point-source pollution index (PNPI) model to explore the dynamic changes of regional potential non-point-source pollution (PNP) and the associated risk due to land use and land cover change (LUCC) over the past 31 years. The Loess Plateau region is mainly composed of cultivated land, grassland and forest, which together account for 93.5% of the watershed area. From 1990 to 2020, extensive soil and water conservation measures were implemented throughout the Loess Plateau region, resulting in a significant reduction in the non-point source pollution risk. Using the quantile classification method, the study area’s PNP risk values were categorized into five distinct levels. The results revealed a polarization phenomenon of PNP risk in the region, with an increase in non-point source pollution risk in the human-influenced areas and a rapid expansion of the very high-risk area. However, the non-point source pollution risk in the upstream water source area of the watershed reduced over the study period. In recent years, the rapid urbanization of the Loess Plateau region has been the primary reason for the rapid expansion of the very high PNP risk area throughout the watershed. This study highlights the significant impact of LUCC on the dynamic changes in PNP risk within the Loess Plateau region, providing crucial insights into future conservation and urban planning policies aimed at enhancing the ecological health and environmental quality of the region.

The response of non-point source pollution to land use change and risk assessment based on model simulation and grey water footprint theory in an agricultural river basin of Yangtze River, China

Non-point source (NPS) pollution has gradually become the main source of water environmental pollution with the rapid economic development during the recent decades, especially for the economically developed Yangtze River Basin of China. Due to rapid urbanization, the pressure of the water environment in the Poyang Lake basin (PYLB) has been increasing. In this study, the spatial and temporal variations of NPS pollution loads including total nitrogen (TN) and total phosphorus (TP) in the PYLB during the period 2003–2012 were firstly investigated by using the SWAT; then the changes in NPS pollution loads under different land use types were identified by using the SWAT, land use transition matrix, statistical analysis and scenario analysis methods; finally, the NPS pollution risk assessment were achieved by means of the grey water footprint theory. The results showed that the NPS pollution loads in the PYLB were concentrated from April to June with a large inter-annual variability. The Gan River basin contributed the largest proportion of pollutants entering into the Poyang lake while the Xiu River basin produced the smallest pollution loads. Under the background of the policy of “Grain for Green Project” since 2002, the areas of cultivated land, grassland and bare land decreased during the period 2000–2010, while those of forest land and settlement increased. As the main source of NPS pollution, the area of cultivated land has decreased, while the unit load intensity under cultivated land has significantly increased. By calculating the annual GWF of TN and TP loads and their corresponding WPL, which all showed an upward trend. The results obtained in this study have practical significance for reducing the NPS pollution loads in the PYLB and controlling the environmental pollution in the Yangtze River.

Agrochemical Input Behavior and Cleaner Production Adoption Willingness of Farmers in Beijing–Tianjin–Hebei, China

Beijing–Tianjin–Hebei is an important agricultural production area in China, and farmers’ agrochemical input behavior directly affects the risk of agricultural non-point source pollution and the effect of green agricultural development. Based on a questionnaire survey and field interview data, this study investigated the agrochemical input behavior of farmers in Beijing–Tianjin–Hebei, and analyzed its influencing factors. Using the Probit model, we carried out an empirical study on farmers’ willingness to invest in cleaner production of agrochemicals from four aspects: farmers’ characteristics, agricultural input, environmental awareness and technical cognition. The results showed that the kinds of fertilizer were mainly compound fertilizer, urea and organic fertilizer, and the fertilization method was mainly surface spreading, accounting for 50.6% of the total surveys; the number of agrochemicals was determined chiefly by agricultural sellers, accounting for 55.5%. The proportion of the guidance from technical departments in Beijing was higher than that of Tianjin and Hebei. The first influencing factor for farmers’ behavior towards agrochemical input was the pursuit of high yield and high profit, accounting for 24.9%, 22.6% and 26.0%, respectively. The guidance of relevant technical departments still did not fully cover the use of agrochemicals. The study also found that factors such as the price of farming materials, the price of agricultural products, family income, farmland facilities, government propaganda, technical training and subsidies all impacted the agrochemical input behavior. Pre-production technical guidance and farmers’ awareness significantly affected the willingness to adopt cleaner production. Technical training was helpful to improve farmers’ willingness to participate actively, and enhancing the pertinence of training played an important role in the adoption of cleaner production technology. In conclusion, the influencing factors of farmers’ agrochemical input in Beijing, Tianjin and Hebei were complex, and the scientific application level still needs to be improved. This paper finally discusses and puts forward some countermeasures and suggestions for agrochemical reduction and efficiency improvement.

Applying water environment capacity to assess the non-point source pollution risks in watersheds

Comprehension of the spatial and temporal characteristics of non-point source (NPS) pollution risk in watersheds is essential for NPS pollution research and scientific management. Although the concept of water functional zones (WFZ) has been considered in the NPS pollution risk assessment process. However, no comprehensive study of the NPS pollution risk has been conducted to effectively protect water quality in watersheds with different water environment capacity. Therefore, this study proposes a new NPS pollution risk assessment method that integrates water functional zoning, receiving water body environmental capacity, and space-time distribution of pollution load for quantifying the impact of pollution discharge from sub-catchment on nearby water body quality. Based on the NPS nutrient loss process modeled by the Soil and Water Assessment Tool (SWAT), this method was used to assess the NPS pollution risk in the Le 'an River Watershed at annual and monthly scales. The results showed that the NPS pollution risk is characterized by seasonal and spatial variability and is influenced clearly by the water environment capacity. High NPS pollution loads are not necessarily high pollution risks. Conversely, a low NPS nutrient pollution load does not represent a low regional risk sensitivity. In addition, NPS risk assessment based on the water environment capacity could also distinguish the differences in risk levels that were masked by similar NPS pollutant loss and the same water function zoning to achieve accurate control of NPS pollution management in watersheds.

Risk Assessment of Nonpoint Source Pollution in the Huaihe River Basin

After years of treatment, the water pollution situation in the Huaihe River Basin (HRB) is still grim, and agricultural nonpoint source pollution has become the leading cause of the problem. However, agricultural nonpoint source pollution in the HRB is complicated due to the compounding effects of multiple factors. In this study, we first applied the export coefficient model to estimate the total nitrogen (TN) and total phosphorus (TP) loads used as two pollution source indicators in HRB. Then we constructed an index evaluation system of nonpoint source pollution risk by coupling the two source indicators with five additional indicators: rainfall erosion, river network distribution, soil erodibility, slope length, and land use. The primary source of TN and TP loads is fertilizer application (81.96%), followed by livestock and poultry breeding (16.3%) and rural domestic wastes (1.74%). The risk assessment results indicate that 66.43% of the HRB is at medium to high risk of nonpoint source pollution, 12.37% is at high risk, and 11.20% is at low risk. Moreover, the medium-to-high-risk areas are mainly concentrated in the Henan and Anhui provinces. In contrast, the medium-risk regions are mainly distributed along the mainstream of the Huaihe River. Finally, the observed water quality categories were used to verify our findings. The controlling areas of nonpoint source pollution in HRB are identified. This study could provide a scientific basis for effectively preventing and treating water pollution in the HRB.

A source-sink landscape approach to mitigation of agricultural non-point source pollution: Validation and application

Optimizing landscape pattern to reduce the risk of non-point source (NPS) pollution is an effective measure to improve river water quality. The “source-sink” landscape theory is a recent research tool for landscape pattern analysis that can effectively integrate landscape type, area, spatial location, and topographic features to depict the spatial heterogeneity of NPS pollution. Based on this theory, we quantitatively analyzed the influence of “source-sink” landscape pattern on the river water quality in one of the most intensive agricultural watersheds in Southeastern China. The results indicated that the proportion of “sink” landscape (68.59%) was greater than that of “source” landscape (31.41%) in the study area. In addition, when elevation and slope increased, the “source” landscape proportion decreased, and the “sink” landscape proportion increased. Nitrogen (N) and phosphorus (P) pollutants in rivers showed significant seasonal and spatial variations. Farmland was the primary source of nitrate nitrogen (NO3−-N) and total nitrogen (TN) pollution, whereas residential land was the primary source of ammonium nitrogen (NH4+-N) and total phosphorus (TP) pollution. Intensively cultivated areas and densely inhabited areas degraded water quality despite high proportions of forest land. The four “source-sink” landscape indices (LWLI, LWLI'e, LWLI's, LWLI'd) had significant positive correlations with NO3−-N and TN and weak correlations with NH4+-N and TP. The capacity of LWLI to quantify the NPS pollution was greater in agricultural areas than in residential areas. The “source-sink” landscape thresholds resulted in abrupt changes in water quality. When LWLI was ∼0.35, the probability of river water quality degradation increased sharply. The results suggest the importance of optimizing the “source-sink” landscape pattern for mitigating agricultural NPS pollution and provide policy makers with adequate new information on the agroecosystem-environmental interface in highly developed agricultural watersheds.

Risk assessment of non-point source pollution in karst reservoirs based on ‘source–sink’ landscape theory

Abstract The quantitative identification of areas at risk for such pollution is conducive to allocating limited government funds to critical areas and the efficient and economical management of water environments. Here, the Baihua Lake watershed, an important drinking water source for Guiyang City, was taken as the study area. The location-weighted landscape contrast index (LCI) and non-point source pollution risk index (NSPRI) were developed based on the ‘source–sink’ landscape theory. The method takes into account the risk of pollution source formation and pollutant transport. A total of 348 natural sub-watersheds were used as assessment units by estimating the nitrogen and phosphorus pollution emission (absorption) potentials of different landscape types in the sub-watersheds and considering the influence of vegetation cover, distance from the reservoir, and slope in the transmission process, a quantitative assessment of Baihua Lake's pollution was carried out; the reliability of the method was verified by comparing the assessment results with measured water quality data and field surveys. The results indicate (1) 132 sub-watersheds (37.93%) dominated by source effects, mainly distributed in Yanshanhong Township, Yeya Township, and the Qinglong Subdistrict, with construction land and farmland as the main landscape types, and 216 sub-watersheds (62.07%) dominated by sink effects, mainly distributed in Zhanjie and Baihuahu Townships, with forests as the primary landscape type. (2) Additionally, 17 sub-watersheds (4.89%) show extremely high risk for non-point source pollution; these watersheds are mainly distributed in the Qinglong Subdistrict and mainly consist of urban residential areas and schools. These sub-watersheds discharge a large volume of sewage, which threatens the water quality of the upper reaches of Baihua Lake and must be managed. (3) The rivers corresponding to relatively high-risk, high-risk, and extremely high-risk sub-watersheds include the Dongmenqiao, Limu, Changchong, and Maixi Rivers.

Nitrogen and Phosphorus Retention Risk Assessment in a Drinking Water Source Area under Anthropogenic Activities

Excessive nitrogen (N) and phosphorus (P) input resulting from anthropogenic activities seriously threatens the supply security of drinking water sources. Assessing nutrient input and export as well as retention risks is critical to ensuring the quality and safety of drinking water sources. Conventional balance methods for nutrient estimation rely on statistical data and a huge number of estimation coefficients, which introduces uncertainty into the model results. This study aimed to propose a convenient, reliable, and accurate nutrient prediction model to evaluate the potential nutrient retention risks of drinking water sources and reduce the uncertainty inherent in the traditional balance model. The spatial distribution of pollutants was characterized using time-series satellite images. By embedding human activity indicators, machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and Multiple Linear Regression (MLR), were constructed to estimate the input and export of nutrients. We demonstrated the proposed model’s potential using a case study in the Yanghe Reservoir Basin in the North China Plain. The results indicate that the area information concerning pollution source types was effectively established based on a multi-temporal fusion method and the RF classification algorithm, and the overall classification low-end accuracy was 92%. The SVM model was found to be the best in terms of predicting nutrient input and export. The determination coefficient (R2) and Root Mean Square Error (RMSE) of N input, P input, N export, and P export were 0.95, 0.94, 0.91, and 0.93, respectively, and 32.75, 5.18, 1.45, and 0.18, respectively. The low export ratios (2.8–3.0% and 1.1–2.2%) of N and P, the ratio of export to input, further confirmed that more than 97% and 98% of N and P, respectively, were retained in the watershed, which poses a pollution risk to the soil and the quality of drinking water sources. This nutrient prediction model is able to improve the accuracy of non-point source pollution risk assessment and provide useful information for water environment management in drinking water source regions.

A framework to assess spatio-temporal variations of potential non-point source pollution risk for future land-use planning

Quantifying spatio-temporal variations of non-point source pollution (NSP) risk induced by land-use change provides insights into effective control of NSP through land-use optimization. However, there is a lack of efficient tools for assessing the variations of NSP risk under various land-use change scenarios. In this study, a framework, which couples the modified potential non-point pollution indicator (PNPI) model with the future land-use simulation (FLUS) model, was developed and applied for assessing the spatio-temporal variations of NSP risk caused by both historical and possible future land-use change in a rapidly urbanized basin (i.e., the upper Beiyun River basin, Beijing) during 1980–2030. The PNPI model is tested with monitored water quality data with an accuracy ratio of 0.75, and the FLUS model can accurately simulate land-use change in the basin (Kappa coefficient = 0.74). Due to rapid urbanization, the area of extremely high-risk zones of NSP went up, even though the area of farmlands decreased, and the area of green spaces increased during 1980–2020. The spatio-temporal variations of NSP risk are different in 2020–2030 under different land-use change scenarios. The ecological security scenario will slow down the growth rate of NSP risk, however, the total area of the extremely high-risk and high-risk zones will increase by 40.17 km2 in 2030 under the rapid urbanization scenario. Coupling the modified PNPI model with the FLUS model provides an efficient tool set to support future land-use planning for NSP control and to assess spatio-temporal variations of NSP risk caused by both historical and future land-use change in data-limited regions.

基于“源-汇”景观的典型半城市化小流域非点源污染风险评价

PDF HTML阅读 XML下载 导出引用 引用提醒 基于“源-汇”景观的典型半城市化小流域非点源污染风险评价 DOI: 10.5846/stxb202109162610 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划课题（2016YFC0502902）；中国科学院战略性先导科技专项（A类）（XDA23030105） Risk assessment of non-point source pollution in a typical semi-urbanized small watershed based on source-sink landscape Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:小流域的水质恶化主要由点源污染和非点源污染引起，随着点源污染控制水平达到一定程度后，非点源污染已成为首要污染源。当前对非点源污染的管控仍存在难监测和难治理的问题，明晰非点源污染发生风险以及背后的原因是亟需解决的问题，因此开展非点源污染风险分析和评价具有重要意义。采用高分辨率影像解译了2010年、2015年和2020年三期厦门市后溪流域土地利用数据；基于"源-汇"景观格局方法计算研究区网格单元的网格污染指数（GPI）；结合土地利用变化数据分析非点源污染风险的时空变异，对风险区成因进行了分析。研究结果表明：当前，"汇"景观占流域面积的67.86%，非点源污染发生风险分布呈现北低南高；十年中，非点源污染风险呈现上升趋势，目前非点源污染发生风险处于低风险水平（GPI=0.27）。通过分析风险区的土地利用构成发现耕地面积的缩减（减少67.08%）和建设用地面积扩张（增加43.02%）是污染风险发生变动的主要原因。计算了风险区转移矩阵，发现非点源污染发生风险区呈现出中高风险区向低风险区和"汇"景观区域转移的趋势。基于"源-汇"景观格局理论计算的网格污染指数（GPI）可以有效地对流域非点源污染风险值进行表征，是评价和分析流域非点源污染发生风险的可用方法。 Abstract:The deterioration of water quality in small watershed is mainly caused by point source pollution and non-point source pollution. With the control level of point source pollution reaching a certain level, non-point source pollution has become the primary pollution source. At present, the control of non-point source pollution is still difficult to monitor and control. It is urgent to clarify the occurrence risk of non-point source pollution and the reasons behind it. Therefore, it is of great significance to conduct the risk analysis and evaluation of non-point source pollution. Land use data of Houxi Basin in Xiamen City in 2010, 2015 and 2020 were interpreted by high-resolution images. The grid pollution index (GPI) of grid units in the study area was calculated based on source-sink landscape pattern method. Combined with land use change data, the spatial and temporal variation of non-point source pollution risk was analyzed, and the causes of risk areas were analyzed. The results showed that sink landscape accounted for 67.86% of the Basin area, and the risk distribution of non-point source pollution was lower in the north and higher in the south. In the past decade, the risk of non-point source pollution showed an increasing trend, and the occurrence risk of non-point source pollution was at a low level (GPI=0.27). By analyzing the land use composition of the risk area, it was found that the decrease of the cultivated land area (67.08%) and the expansion of construction land area (43.02%) were the main reasons for the change of pollution risk. The risk area transfer matrix was calculated. It was found that the risk area of non-point source pollution presented a trend of transfer from the middle and high-risk area to the low-risk area and the sink landscape area. The grid pollution index (GPI) calculated based on the source-sink landscape pattern theory can effectively characterize the risk value of non-point source pollution in a watershed, and is a useful method to evaluate and analyze the occurrence risk of non-point source pollution in a watershed. 参考文献 相似文献 引证文献

Evaluating the risks of spatial and temporal changes in nonpoint source pollution in a Chinese river basin

In watershed management, it is of great importance to evaluate the risks of nonpoint source (NPS) pollution. In this study, the Nonpoint Source Pollution Risk Index (NSPRI), a multi-factor NPS risk assessment model that was based on the source-sink landscape theory, was proposed and applied in Muzhuhe River Basin, Shandong, China to (1) highlight spatial and temporal variations in the risks from nitrogen and phosphorus losses, and (2) identify how the basin characteristics influenced the risk of nutrient loss. According to the analysis on land use change, the study area is featured with high proportions of forest and agricultural land uses; the area of urban and industrial land had increased considerably from 2000 and 2018. Based on the division of the calculated risk indices on subbasin scale, the area with extremely high risks has decreased from 56,442 ha to 43,922 ha. The average and coefficient of variation (CV) values of NSPRI in the river basin have dropped from 1.3 to 1.1, and from 78.2% to 48.9%, respectively. The distribution of NSPRI suggested an increase in spatial clustering and improvements in the ecological balance. Correlation analysis of the Soil and Water Assessment Tool (SWAT) model (R2 > 0.68, ENS > 0.59) and NSPRI indicated the applicability of the method used (r > 0.84, p < 0.01). Analysis on the impact of metrics of land use composition, landscape, and environmental settings on NSPRI indicated that the water quality was more significantly correlated with land use composition, landscape pattern and vegetation cover than with flow path distance, soil erodibility, and rainfall erosivity. Moreover, results of redundancy analysis revealed that nutrient loss risk was better explained by land use compositions than by landscape configuration. The assessment method provided scientific support for NPS pollution control from the perspective of source-sink landscape theory.

Monitoring the Landscape Pattern and Characteristics of Non-Point Source Pollution in a Mountainous River Basin.

This study aimed to assess the relationship between the landscape patterns and non-point source (NPS) pollution distribution in Qixia County, China. The sub-basin classification was conducted based on a digital elevation model and Landsat8 satellite images. Water samples were collected from each sub-basin, andtheir water quality during the wet and dry seasons was estimated. The correlation between the landscape indices and water pollution indicators was determined by Pearson analysis. The location-weighted landscape contrast index (LWLCI) was calculated based on the “source-sink” theory. Qixia was further divided into five sections based on the LWLCI score to illustrate the potential risk of NPS pollution. The results showed that the water quality in Qixia County was generally good. Cultivated land, orchards, construction areas, and unused land were positively correlated with the water pollution index and weredesignated as the “source” landscape categories, while forests, grasslands, and water bodies, which were negatively correlated with water pollution, were the “sink” landscapes; the LWCI was high in 36.94% of the study area. In these areas, measures such as increasing vegetation buffer zones are necessary to decrease the sediment and nutrient loads carried by precipitation.

Water Resource Risk Assessment Based on Non-Point Source Pollution

As one of the most important causes of water quality deterioration, NPS (non-point source) pollution has become an urgent environmental and livelihood issue. To date, there have been only a few studies focusing on NPS pollution conforming to the estimation, and the pollution sources are mainly concentrated in nitrogen and phosphorus nutrients. Unlike studies that only consider the intensity of nitrogen and phosphorus loads, the NPS pollution risk for the China’s Fuxian Lake Basin was evaluated in this study by using IECM (Improve Export Coefficient Model) and RUSLE (Revised Universal Soil Loss Equation) models to estimate nitrogen and phosphorus loads and soil loss and by using a multi-factor NPS pollution risk assessment index established on the basis of the data mentioned above. First, the results showed that the load intensity of nitrogen and phosphorus pollution in the Fuxian Lake Basin is low, so agricultural production and life are important sources of pollution. Second, the soil loss degree of erosion in the Fuxian Lake is mild, so topography is one of the most important factors affecting soil erosion. Third, the risk of NPS pollution in the Fuxian Lake Basin is at a medium level and its spatial distribution characteristics are similar to the intensity characteristics of nitrogen and phosphorus loss. Nitrogen, phosphorus, sediment, and mean concentrations are important factors affecting NPS pollution. These factors involve both natural and man-made environments. Therefore, it is necessary to comprehensively consider the factors affecting NPS in order to assess the NPS risk more accurately, as well as to better solve the problem of ecological pollution of water resources and to allow environmental restoration.

Analysis of Spatial-Temporal Variation Characteristics of Potential Non-point Source Pollution Risks in the Upper Beiyun River Basin Using Different Weighting Methods

Non-point source pollution has become an important factor affecting the aquatic ecological environment and human health, and the analysis of spatial-temporal variations in non-point source pollution risks is an important prerequisite for pollution control. Based on land-use and land-cover data from 1980 to 2020, the potential non-point source pollution index (PNPI) model was applied in the upper Beiyun River Basin using different weighting methods. The results showed that:① The potential risk of non-point source pollution is high in the southeast and low in the northwest of the basin. Between 1980 and 2020, the total area of extremely high-risk and high-risk non-point source pollution regions showed a decreasing trend, and the main types of land use for extremely high-risk and high-risk regions gradually evolved from paddy fields, drylands, and orchards to urban and rural residential land; ② The weighting of the land use index determined by the mean-square deviation decision, entropy, coefficient of variation, and expert scoring methods was largest among the three PNPI indices, with average weightings of 0.46, 0.53, 0.45, and 0.48, respectively. However, the weightings for runoff and distance indices determined by different weighting methods were notably different, and the proportions of regions with different levels of non-point source pollution risk also varied; ③ The exponential function method, which describes the relationship between source factors and transport factors by constructing the exponential functions of land use, runoff, and distance indices, provided results that are more consistent with the spatial distribution characteristics of non-point source pollution risk in the basin. The proportions of extremely low-risk and extremely high-risk regions are 54.22% and 6.23%, respectively. These results provide scientific reference for risk analysis and the control of non-point source pollution in this basin.

The conditions of emergence of cooperation to prevent the risk of diffuse pollution from agriculture: a case study comparison from France

Groundwater resources are essential for the supply of drinking water in the European Union but significant uncertainties surround their quality. In this context, our article analyzes the factors that influence the cooperation of key stakeholders to prevent the risk of diffuse pollution from agriculture. We built on the institutional rational choice approach and the social-ecological system framework to design our analytical framework and hypotheses. We applied this analytical framework to two cases of cooperation involving drinking water suppliers and farmers in the management of the risk of non-point source pollution from agriculture in France. Our results show that cooperation emerges when the water resource is important for water suppliers to take on the leadership. Our results also characterize the role played by uncertainties and risk in getting stakeholders involved, and finally, suggest that cooperation depends on the size and predictability of the resource system.